Instructions to use yarongef/DistilProtBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yarongef/DistilProtBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yarongef/DistilProtBert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yarongef/DistilProtBert") model = AutoModelForMaskedLM.from_pretrained("yarongef/DistilProtBert") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -9,6 +9,7 @@ datasets:
|
|
| 9 |
|
| 10 |
# DistilProtBert model
|
| 11 |
|
| 12 |
-
Distilled protein language of [ProtBert](https://huggingface.co/Rostlab/prot_bert)
|
| 13 |
-
|
| 14 |
-
|
|
|
|
|
|
| 9 |
|
| 10 |
# DistilProtBert model
|
| 11 |
|
| 12 |
+
Distilled protein language of [ProtBert](https://huggingface.co/Rostlab/prot_bert).
|
| 13 |
+
In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective and it only works with capital letter amino acids.
|
| 14 |
+
|
| 15 |
+
# Model description
|